Asks for

The ad says“Hands-on experience with computational chemistry or simulation tools, such as ADMET models, docking, binding prediction, or molecular generative models”
The ad says“Hands-on experience with computational chemistry or simulation tools, such as ADMET models, docking, binding prediction, or molecular generative models”
The ad says“Familiarity with common techniques in target discovery, gene expression analysis, pathway inference, clustering, or statistical modeling”
The ad says“Strong experience in machine learning, computational biology, or a related scientific ML field”
The ad says“Hands-on experience with computational chemistry or simulation tools, such as ADMET models, docking, binding prediction, or molecular generative models”
The ad says“Hands-on experience with computational chemistry or simulation tools, such as ADMET models, docking, binding prediction, or molecular generative models”
The ad says“Familiarity with common techniques in target discovery, gene expression analysis, pathway inference, clustering, or statistical modeling”
The ad says“Strong experience in machine learning, computational biology, or a related scientific ML field”
The ad says“Hands-on experience with computational chemistry or simulation tools, such as ADMET models, docking, binding prediction, or molecular generative models”
The ad says“Familiarity with common techniques in target discovery, gene expression analysis, pathway inference, clustering, or statistical modeling”
The ad says“Proficiency in Python and scientific computing libraries”
The ad says“Proficiency in Python and scientific computing libraries”
The ad says“Familiarity with common techniques in target discovery, gene expression analysis, pathway inference, clustering, or statistical modeling”
The ad says“Familiarity with common techniques in target discovery, gene expression analysis, pathway inference, clustering, or statistical modeling”

ABOUT MITHRL

We imagine a world where new medicines reach patients in months, not years, and where scientific breakthroughs happen at the speed of thought.

Mithrl is building the world’s first commercially available AI Co-Scientist. It is a discovery engine that transforms messy biological data into insights in minutes. Scientists ask questions in natural language, and Mithrl responds with real analysis, novel targets, hypotheses, and patent-ready reports.

Our traction speaks for itself:

12X year-over-year revenue growth
Trusted by leading biotechs and big pharma across three continents
Driving real breakthroughs from target discovery to patient outcomes.

ABOUT THE ROLE

We are hiring an ML Engineer, Analysis and Simulation to build the core analytical and reasoning layer behind the Mithrl AI Co-Scientist. Your work will define how the AI interprets biological datasets, generates scientific conclusions, and orchestrates downstream simulation tools for drug discovery.

You will develop the reusable analysis modules that Mithrl runs for every dataset, and you will design multi step agentic workflows that combine statistical analysis, biological reasoning, and computational modeling. You will also integrate and experiment with simulation tools for small molecule discovery, such as ADMET prediction, docking scoring, Boltzmann generators and related computational chemistry engines.

This is the role that makes the AI Co-Scientist smart. If you have a strong background in ML, computational biology, and scientific analysis workflows, and you want to shape how AI reasons about biological systems, this is an exceptional opportunity.

WHAT YOU WILL DO

Build AI driven analysis agents that perform biological reasoning across a wide range of datasets
Develop the standard analysis suite for each dataset, including modules for differential expression, pathway analysis, feature importance, clustering, scoring, enrichment, and mechanism-of-action interpretation
Build multi step workflows that combine ML models, statistical logic, and biological knowledge to produce high confidence insights
Design and implement agentic reasoning strategies that allow Mithrl to run dozens analyses per dataset and synthesize the outputs into a coherent scientific narrative
Integrate simulation and modeling tools for small molecule drug discovery, including ADMET prediction, docking scoring, generative chemistry tools, structure based modeling, and related computational frameworks
Collaborate with the data engineering, bioinformatics, and curation teams to ensure analysis modules operate on clean and consistent data
Validate results, benchmark pipelines, and ensure scientific accuracy and reproducibility of all analyses
Contribute to the long term architecture for how the AI Co-Scientist performs reasoning, hypothesis testing, and simulation

WHAT YOU BRING

Required Qualifications

Strong experience in machine learning, computational biology, or a related scientific ML field
Experience developing analysis modules for biological or scientific datasets
Familiarity with common techniques in target discovery, gene expression analysis, pathway inference, clustering, or statistical modeling
Hands-on experience with computational chemistry or simulation tools, such as ADMET models, docking, binding prediction, or molecular generative models
Proficiency in Python and scientific computing libraries
Experience designing multi step reasoning or workflow based ML pipelines
Ability to translate messy scientific questions into structured ML or analytical workflows
Strong communication skills and comfort collaborating with cross functional scientific and engineering teams

Nice to Have

Experience with LLM powered scientific agents or multi agent architectures
Familiarity with phenotype based discovery, multi modal integration, or systems biology
Background in computational chemistry or structure based drug discovery
Experience with biological ontologies, curated knowledge graphs, or pathway databases
Prior experience in a tech bio company, biotech R&D group, or scientific platform team

WHAT YOU WILL LOVE AT MITHRL

High ownership: You will define how the AI Co-Scientist thinks and reasons about biology
Impact: You will work at the intersection of ML, biology, and simulation, with direct impact on real discovery programs
Team: Join a tight-knit, talent-dense team of engineers, scientists, and builders
Culture: We value consistency, clarity, and hard work. We solve hard problems through focused daily execution
Speed: We ship fast (2x/week) and improve continuously based on real user feedback
Location: Beautiful SF office with a high-energy, in-person culture
Benefits: Comprehensive PPO health coverage through Anthem (medical, dental, and vision) + 401(k) with top-tier plans

We encourage you to apply even if you do not believe you meet every single qualification. Not all strong candidates will meet every single qualification as listed. Research shows that people who identify as being from underrepresented groups are more prone to experiencing imposter syndrome and doubting the strength of their candidacy, so we urge you not to exclude yourself prematurely and to submit an application if you're interested in this work. We think AI systems like the ones we're building have enormous social and ethical implications. We think this makes representation even more important, and we strive to include a range of diverse perspectives on our team.

Asks for

Values